code: def testTwoClassLogLikelihoodVersusOldImplementation(self):
      def alt_two_class_log_likelihood_impl(predictions, labels):
          float_labels = tf.cast(labels, dtype=tf.float64)
          float_predictions = tf.cast(
              tf.squeeze(predictions), dtype=tf.float64
          )
          # likelihood should be just p for class 1, and 1 - p for class 0.
          # signs is 1 for class 1, and -1 for class 0
          signs = 2 * float_labels - tf.ones_like(float_labels)
          # constant_term is 1 for class 0, and 0 for class 1.
          constant_term = tf.ones_like(float_labels) - float_labels
          likelihoods = constant_term + signs * float_predictions
          log_likelihoods = tf.log(likelihoods)
          avg_log_likelihood = tf.reduce_mean(log_likelihoods)
          return avg_log_likelihood
  
      predictions = np.random.rand(1, 10, 1)
      targets = np.random.randint(2, size=10)
      with self.test_session() as session:
          new_log_likelihood, _ = metrics.two_class_log_likelihood(
              predictions, targets
          )
          alt_log_likelihood = alt_two_class_log_likelihood_impl(
              predictions, targets
          )
          new_impl, alt_impl = session.run(
              [new_log_likelihood, alt_log_likelihood]
          )
      self.assertAlmostEqual(new_impl, alt_impl)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.utils import metrics
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling the MetricsTest obj's underlying member method code with example usage: obj.testTwoClassLogLikelihoodVersusOldImplementation() and returns: avg_log_likelihood